Induced Earthquakes and Satlwater Disposal

Final Project

Advance Geographic Information Sytem
GEOG 7940
Md Mohimanul Islam

Introduction

Starting around 2009, there was a noticeable uptick in the frequency of small to moderate earthquakes across large portions of the central and eastern United States. In the rencet decades, certain areas of Oklahoma have seen significant rises in the occurrence of small to medium-sized earthquakes. It is a commonly acknowledged observation that a significant portion of this seismic activity appears to be connected to the increase in injection wells and hydraulic fracturing activities within oil and gas fields (Frohlich et al., 2012; Horton, 2012; Kim, 2013; McGarr et al., 2015). Typically, produced water resulting from oil and gas exploration activities is either reintroduced into the oil-producing formation during water-flooding enhanced oil recovery (EOR) processes, or it is disposed of in specialized saltwater disposal (SWD) wells. These wells commonly inject the water into saline aquifers, which are sedimentary formations characterized by relatively high porosity and permeability.

When water is injected into the underground reservoir, the fluid pressure increases. This causes reduction of the effective normal stress on a fault. As the effective normal stress decreases, it serves to loosen a fault, possibly initiating the release of stored strain energy on an existing fault that is already near failure. An earthquake occurring on such a fault under critical stress due to fluid injection is known as a triggered earthquake. Over time, strain energy builds up on a fault as part of natural geological processes. The alteration in pressure caused by fluid injection merely prompts its release and results a trigggerd earthquake.

This project aims to investigate injection-related seismicity through geospatial analysis. I have chosen the state of Oklahoma due to its significant increase in seismic activity in recent years. In this endeavor, I've developed a Jupyter notebook capable of conducting similar analyses for other states affected by induced seismicity, such as Kansas and Illinos.

Workflows:

Import necessary python modules

Dowload Earthquakes data form USGS

Analysis and Mapping

Interpretatoin

# import os module
import sys, os

# setting up the workspace 
import os

# import arcpy module
import arcpy

# import env module
from arcpy import env

# import mp module
from arcpy import mp

# import url builder libaray 

import urllib.request   

# import csv module
import csv
# import datetime module
from datetime import datetime

Configure the current workspace environmental variables

#path to the project
BaseDirectory = os.getcwd()

## Set the overwrite variable.
arcpy.env.overwriteOutput = True

GDBName='project.gdb'

# set the project data path
project_data_path=os.path.join(BaseDirectory,'projectdata')

# Create the geodatabase
#arcpy.CreateFileGDB_management(BaseDirectory, GDBName)

## Set a variable to the path to the ADAExData.gdb.
GDBpath = os.path.join(BaseDirectory,GDBName)

## Set the current workspace
env.workspace = GDBpath

## Set the scratch workspace to be a gdb named ScratchOutput.gdb in the ADAExData directory.
arcpy.env.scratchWorkspace = os.path.join(BaseDirectory,'Projectdata','ScratchOutput.gdb')

Project database

The project database contain sevearal datasets and feature classes necessay for this project.

# Project database containt US sate polygon 
feature_classes = arcpy.ListFeatureClasses()
print(feature_classes)

['counties', 'states_wgs84', 'OK_class_II_injection_wells', 'eq_Oklahoma', 'OK_state', 'US_faults', 'states', 'Oklahoma', 'grids', 'eq_per_grids', 'eqOK', 'Oklahoma_eq_density', 'Oklahoma_faults', 'well_buffer', 'AR_ClassII_InjectionWells', 'CA_ClassII_InjectionWells', 'IL_ClassII_InjectionWells', 'IN_ClassII_InjectionWells', 'KS_ClassII_InjectionWells', 'UT_ClassII_InjectionWells']

We can see the geodatabase contains several featureclases:

• states_wgs84 - this feature class contains all the state polyons of US with necessay informaiton in the attribute table

• state_name_class_II_injection_wells - these are the set of feature classes contains the location of the Class II injeciton wells. These data are dowloaded form "https://www.fractracker.org/waste/".

• us_faults - Quaternary Fault database from USGS that demonstrate geological evidence of coseismic surface deformation in large earthquakes during the past 1.6 million years (Ma). source link : https://www.usgs.gov/programs/earthquake-hazards/faults

• Please only select the state that have the available injection well data.

• All the porject maps are stored in Figure folder

Define state name and claculate its bounding geometry

• In this step, the stae of interest (Oklahoma) is defined and its extent is calculed form the state polyogn

# Define the spatial reference of WGS84
sr = arcpy.SpatialReference(4326)
stateFc = "states_wgs84"
stateFc_path=os.path.join(GDBpath,stateFc)

# the name of state (we will put focus on Oklahoma, change the sate name if you want to analize different state)
state_name='Oklahoma'   

# path to the ouput map directory
out_maps=os.path.join(BaseDirectory,"Figures")   

# now get the extent of th e

# Create a search cursor to iterate through the features in the shapefile
with arcpy.da.SearchCursor(stateFc, ['SHAPE@', 'STATE_NAME', 'STATE_ABBR']) as cursor:
    min_lon = float('inf')
    min_lat = float('inf')
    max_lon = float('-inf')
    max_lat = float('-inf')

    for row in cursor:
        if row[1] == state_name:
            # Get the extent (bounding box) of the feature's geometry
            extent = row[0].extent

            # Update min and max latitude and longitude values
            min_lon = min(min_lon, extent.XMin)
            min_lat = min(min_lat, extent.YMin)
            max_lon = max(max_lon, extent.XMax)
            max_lat = max(max_lat, extent.YMax)
            # get the abbriviated state code 
            state_name_abr=row[2]

# Print the min and max latitude and longitude values
print("Minimum Latitude:", min_lat)
print("Maximum Latitude:", max_lat)
print("Minimum Longitude:", min_lon)
print("Maximum Longitude:", max_lon)
print("state short name:", state_name_abr)

Minimum Latitude: 33.62114259700007
Maximum Latitude: 37.00148467400004
Minimum Longitude: -102.99771884899997
Maximum Longitude: -94.42855910599997
state short name: OK

Retrive the earthquake datasets from USGS Eathquake (NEIC) catalogue

• In this part the earthquake datsets are downloaded form the USGS catalogue by specifying the necearray paremeters
• To aid the analysis, I have downloaded the eathquake form 1960 to 2023.
• The output data will be saved as a text file (earthquakes_statename)

# retrive the earthquake datasets from USGS Eathquake (NEIC) catalog 

# setting the other parameter
st_yr='1960-01-01'    # start year 
end_yr='2023-01-01'   # end year
mag_min=2.0   # minimum magnitude
mag_max=9.0   # maximumum magnitude


url = "https://earthquake.usgs.gov/fdsnws/event/1/query?format=text&starttime=%s&endtime=%s&minmagnitude\
=%2.1f&maxmagnitude=%2.1f&minlatitude=%4.1f&minlongitude=%5.1f&maxlatitude=%4.1f&maxlongitude=%5.1f" \
%(st_yr, end_yr, mag_min, mag_max, min_lat, min_lon, max_lat, max_lon)
outfile_name = 'earthquakes_' +str(state_name) +'.txt'
outfile_path=os.path.join(project_data_path,outfile_name)

print(url)
# download the dataset
urllib.request.urlretrieve(url, outfile_path)

https://earthquake.usgs.gov/fdsnws/event/1/query?format=text&starttime=1960-01-01&endtime=2023-01-01&minmagnitude=2.0&maxmagnitude=9.0&minlatitude=33.6&minlongitude=-103.0&maxlatitude=37.0&maxlongitude=-94.4

Now make a piont feature class earthquakes location,time and magnitude

# create new point featureclass
eqFC = "eq_%s" % state_name
eqFC_path=os.path.join(GDBpath,eqFC)
arcpy.management.CreateFeatureclass(out_path=GDBpath,out_name=eqFC,geometry_type="POINT",spatial_reference=sr)


# define fields to add
fields = [["DateTime",  "DATE"],
              ["Latitude",    "FLOAT"],
              ["Longitude",  'FLOAT'],
              ["Depth",    "FLOAT"],
              ["Magnitude",  "DOUBLE"]]
              
#add fields
for fieldName, fieldType in fields:
    #print(fieldName)
    #print(fieldType)
    arcpy.management.AddField(eqFC, fieldName, fieldType)

# poplute the feature class from the txt file 
#(its a lot of entries, might take several minultes)

# Open the text file and insert data into the point feature class

with open(outfile_path, 'r') as file:
    # Skip the header line
    next(file)  
    for line in file:
             
        # Split the line using delimiter
        fields = line.strip().split("|")
        time=fields[1]
        eq_date = datetime.strptime(time, "%Y-%m-%dT%H:%M:%S.%f")
        lat=float(fields[2])
        lon=float(fields[3])
        
        depth=float(fields[4])
        mag=float(fields[10])
        XY=(float(lon), float(lat))
        row = [XY, lat, lon, depth, mag, eq_date]
        print(row)
        
        with arcpy.da.InsertCursor(eqFC, ["SHAPE@XY", "Latitude","Longitude", "Depth", "Magnitude", "DateTime"]) as cursor:
                                   cursor.insertRow(row)
                           

[(-97.18883333, 35.85383333), 35.85383333, -97.18883333, 7.51, 2.61, datetime.datetime(2022, 12, 31, 8, 52, 11, 169000)]
[(-98.20483333, 35.45033333), 35.45033333, -98.20483333, 6.86, 2.03, datetime.datetime(2022, 12, 29, 9, 38, 58, 350000)]
[(-98.958, 36.54083333), 36.54083333, -98.958, 6.79, 2.45, datetime.datetime(2022, 12, 28, 14, 53, 31, 120000)]
[(-95.54233333, 35.12583333), 35.12583333, -95.54233333, 7.28, 2.26, datetime.datetime(2022, 12, 27, 13, 9, 13, 390000)]
[(-98.9605, 36.5425), 36.5425, -98.9605, 6.22, 2.24, datetime.datetime(2022, 12, 26, 15, 44, 37, 340000)]
[(-98.95816667, 36.54266667), 36.54266667, -98.95816667, 6.64, 2.3, datetime.datetime(2022, 12, 26, 12, 52, 36, 740000)]
[(-98.95916667, 36.54233333), 36.54233333, -98.95916667, 6.67, 2.36, datetime.datetime(2022, 12, 26, 5, 59, 1, 990000)]
[(-98.0205, 35.283), 35.283, -98.0205, 7.62, 2.92, datetime.datetime(2022, 12, 23, 11, 7, 18, 370000)]
[(-97.8995, 34.99733333), 34.99733333, -97.8995, 9.17, 2.07, datetime.datetime(2022, 12, 20, 19, 33, 26, 570000)]
[(-98.95916667, 36.54083333), 36.54083333, -98.95916667, 6.74, 2.25, datetime.datetime(2022, 12, 20, 16, 10, 28, 149000)]
[(-98.96533333, 36.53816667), 36.53816667, -98.96533333, 6.69, 2.52, datetime.datetime(2022, 12, 17, 15, 51, 41, 490000)]
[(-98.96566667, 36.53616667), 36.53616667, -98.96566667, 6.67, 2.42, datetime.datetime(2022, 12, 17, 15, 50, 22, 800000)]
[(-98.9615, 36.5415), 36.5415, -98.9615, 6.71, 3.14, datetime.datetime(2022, 12, 17, 15, 48, 47, 560000)]
[(-98.04016667, 35.25133333), 35.25133333, -98.04016667, 6.97, 2.12, datetime.datetime(2022, 12, 14, 20, 24, 30, 160000)]
[(-97.62216667, 34.99616667), 34.99616667, -97.62216667, 9.54, 2.07, datetime.datetime(2022, 12, 14, 17, 12, 12, 599000)]
[(-98.672, 35.70783333), 35.70783333, -98.672, 7.78, 2.4, datetime.datetime(2022, 12, 13, 16, 13, 57, 159000)]
[(-98.02616667, 35.27616667), 35.27616667, -98.02616667, 7.73, 2.62, datetime.datetime(2022, 12, 13, 4, 9, 13, 519000)]
[(-98.66633333, 35.70983333), 35.70983333, -98.66633333, 7.03, 2.34, datetime.datetime(2022, 12, 12, 19, 18, 37, 39000)]
[(-95.5575, 35.11783333), 35.11783333, -95.5575, 6.96, 2.26, datetime.datetime(2022, 12, 12, 2, 8, 34, 420000)]
[(-98.03166667, 35.24766667), 35.24766667, -98.03166667, 6.71, 2.1, datetime.datetime(2022, 12, 11, 7, 12, 38, 329000)]
[(-97.56066667, 35.96983333), 35.96983333, -97.56066667, 19.17, 2.71, datetime.datetime(2022, 12, 10, 21, 51, 43, 359000)]
[(-97.67366667, 36.69633333), 36.69633333, -97.67366667, 5.0, 3.04, datetime.datetime(2022, 12, 10, 4, 26, 23, 210000)]
[(-98.02716667, 35.2575), 35.2575, -98.02716667, 6.75, 2.65, datetime.datetime(2022, 12, 7, 16, 51, 3, 669000)]
[(-98.02233333, 35.258), 35.258, -98.02233333, 6.41, 2.04, datetime.datetime(2022, 12, 7, 16, 22, 9, 689000)]
[(-98.02233333, 35.25783333), 35.25783333, -98.02233333, 6.7, 2.2, datetime.datetime(2022, 12, 6, 21, 58, 26, 769000)]
[(-97.61983333, 34.949), 34.949, -97.61983333, 7.0, 2.79, datetime.datetime(2022, 12, 6, 21, 50, 30, 559000)]
[(-98.0275, 35.2555), 35.2555, -98.0275, 7.38, 2.83, datetime.datetime(2022, 12, 6, 18, 31, 29, 839000)]
[(-98.035, 35.24966667), 35.24966667, -98.035, 7.32, 2.14, datetime.datetime(2022, 12, 6, 17, 35, 36, 9000)]
[(-98.02416667, 35.25916667), 35.25916667, -98.02416667, 6.25, 2.51, datetime.datetime(2022, 12, 6, 2, 39, 23, 300000)]
[(-96.824, 35.49033333), 35.49033333, -96.824, 13.6, 2.1, datetime.datetime(2022, 11, 29, 4, 0, 54, 679000)]
[(-98.01716667, 35.28283333), 35.28283333, -98.01716667, 6.75, 2.9, datetime.datetime(2022, 11, 28, 2, 33, 36, 759000)]
[(-98.0065, 35.2845), 35.2845, -98.0065, 6.47, 2.35, datetime.datetime(2022, 11, 27, 21, 42, 14, 250000)]
[(-98.02683333, 35.27883333), 35.27883333, -98.02683333, 7.5, 2.22, datetime.datetime(2022, 11, 27, 7, 44, 27, 440000)]
[(-98.01083333, 35.28533333), 35.28533333, -98.01083333, 7.33, 2.8, datetime.datetime(2022, 11, 25, 16, 37, 54, 880000)]
[(-97.2635, 34.50916667), 34.50916667, -97.2635, 0.0, 2.02, datetime.datetime(2022, 11, 22, 20, 8, 30, 800000)]
[(-96.22066667, 34.44683333), 34.44683333, -96.22066667, 0.0, 2.25, datetime.datetime(2022, 11, 21, 11, 41, 13, 630000)]
[(-97.89333333, 35.9385), 35.9385, -97.89333333, 6.8, 2.25, datetime.datetime(2022, 11, 20, 8, 8, 23, 230000)]
[(-97.58866667, 35.87416667), 35.87416667, -97.58866667, 7.0, 2.44, datetime.datetime(2022, 11, 20, 4, 45, 27, 539000)]
[(-95.51, 35.08316667), 35.08316667, -95.51, 7.0, 2.32, datetime.datetime(2022, 11, 20, 1, 6, 56, 530000)]
[(-98.02088165, 35.28001404), 35.28001404, -98.02088165, 3.643429279, 2.15, datetime.datetime(2022, 11, 20, 0, 34, 49, 924000)]
[(-97.58733333, 35.87483333), 35.87483333, -97.58733333, 7.7, 2.04, datetime.datetime(2022, 11, 19, 16, 15, 18, 960000)]
[(-96.2065, 34.9255), 34.9255, -96.2065, 2.85, 2.22, datetime.datetime(2022, 11, 19, 5, 31, 55, 299000)]
[(-97.223, 34.49383333), 34.49383333, -97.223, 0.0, 2.02, datetime.datetime(2022, 11, 18, 19, 17, 34, 560000)]
[(-96.21516667, 34.9205), 34.9205, -96.21516667, 5.83, 2.01, datetime.datetime(2022, 11, 18, 4, 40, 25, 589000)]
[(-97.58933333, 35.87416667), 35.87416667, -97.58933333, 9.75, 2.64, datetime.datetime(2022, 11, 18, 1, 49, 19, 379000)]
[(-97.61666667, 34.95333333), 34.95333333, -97.61666667, 10.5, 2.42, datetime.datetime(2022, 11, 17, 21, 19, 19, 780000)]
[(-98.15633333, 36.36716667), 36.36716667, -98.15633333, 7.0, 2.3, datetime.datetime(2022, 11, 16, 23, 42, 33, 350000)]
[(-98.01733333, 35.28233333), 35.28233333, -98.01733333, 7.9, 2.92, datetime.datetime(2022, 11, 15, 10, 49, 13, 730000)]
[(-98.0185, 35.29216667), 35.29216667, -98.0185, 7.29, 2.02, datetime.datetime(2022, 11, 14, 11, 1, 52, 469000)]
[(-98.01466667, 35.28816667), 35.28816667, -98.01466667, 7.65, 2.23, datetime.datetime(2022, 11, 14, 3, 15, 11, 189000)]
[(-98.00033333, 35.28733333), 35.28733333, -98.00033333, 6.4, 2.18, datetime.datetime(2022, 11, 13, 11, 26, 0, 850000)]
[(-98.65516667, 35.7125), 35.7125, -98.65516667, 6.93, 2.08, datetime.datetime(2022, 11, 12, 21, 43, 28, 910000)]
[(-98.01166667, 35.28983333), 35.28983333, -98.01166667, 7.1, 2.18, datetime.datetime(2022, 11, 12, 11, 22, 24, 609000)]
[(-98.6535, 35.72383333), 35.72383333, -98.6535, 5.12, 2.09, datetime.datetime(2022, 11, 12, 5, 44, 54, 780000)]
[(-98.64916667, 35.72066667), 35.72066667, -98.64916667, 5.98, 2.32, datetime.datetime(2022, 11, 12, 1, 11, 3, 330000)]
[(-97.99133333, 35.30116667), 35.30116667, -97.99133333, 6.34, 2.07, datetime.datetime(2022, 11, 11, 2, 47, 30, 879000)]
[(-98.96333333, 36.54116667), 36.54116667, -98.96333333, 6.67, 2.42, datetime.datetime(2022, 11, 11, 0, 52, 42, 240000)]
[(-98.48466667, 34.72283333), 34.72283333, -98.48466667, 20.94, 2.76, datetime.datetime(2022, 11, 10, 23, 25, 48, 950000)]
[(-97.89316667, 35.407), 35.407, -97.89316667, 5.83, 2.07, datetime.datetime(2022, 11, 10, 9, 48, 59, 200000)]
[(-97.62583333, 34.944), 34.944, -97.62583333, 10.88, 2.71, datetime.datetime(2022, 11, 9, 21, 51, 19, 190000)]
[(-98.96366667, 36.53883333), 36.53883333, -98.96366667, 6.64, 2.51, datetime.datetime(2022, 11, 7, 6, 9, 33, 679000)]
[(-96.0045, 34.66583333), 34.66583333, -96.0045, 23.54, 2.55, datetime.datetime(2022, 11, 6, 22, 54, 8, 509000)]
[(-98.95966667, 36.542), 36.542, -98.95966667, 6.44, 2.39, datetime.datetime(2022, 11, 6, 15, 52, 33, 780000)]
[(-98.96333333, 36.54016667), 36.54016667, -98.96333333, 6.7, 2.61, datetime.datetime(2022, 11, 6, 15, 37, 38, 369000)]
[(-97.00183333, 35.49583333), 35.49583333, -97.00183333, 7.12, 2.13, datetime.datetime(2022, 11, 6, 5, 20, 51, 39000)]
[(-98.96516667, 36.53783333), 36.53783333, -98.96516667, 6.81, 2.01, datetime.datetime(2022, 11, 5, 15, 31, 9, 929000)]
[(-98.96666667, 36.5395), 36.5395, -98.96666667, 6.73, 2.41, datetime.datetime(2022, 11, 1, 12, 24, 50, 159000)]
[(-97.3125, 36.46716667), 36.46716667, -97.3125, 7.23, 2.31, datetime.datetime(2022, 10, 31, 20, 50, 39, 119000)]
[(-97.31216667, 36.46666667), 36.46666667, -97.31216667, 7.0, 2.29, datetime.datetime(2022, 10, 31, 20, 43, 22, 769000)]
[(-97.346, 34.5475), 34.5475, -97.346, 2.56, 2.02, datetime.datetime(2022, 10, 30, 17, 49, 55, 359000)]
[(-97.24233333, 34.522), 34.522, -97.24233333, 0.97, 2.04, datetime.datetime(2022, 10, 30, 7, 37, 34, 420000)]
[(-101.7604417, 35.17181396), 35.17181396, -101.7604417, 7.947794596, 2.3, datetime.datetime(2022, 10, 28, 23, 49, 46, 214000)]
[(-97.8905, 35.41066667), 35.41066667, -97.8905, 4.98, 2.37, datetime.datetime(2022, 10, 28, 14, 30, 38, 30000)]
[(-101.7660757, 35.16906738), 35.16906738, -101.7660757, 6.113688151, 2.5, datetime.datetime(2022, 10, 28, 13, 33, 21, 7000)]
[(-97.26133333, 34.52533333), 34.52533333, -97.26133333, 0.0, 2.15, datetime.datetime(2022, 10, 28, 9, 7, 41, 850000)]
[(-97.371, 34.5375), 34.5375, -97.371, 4.91, 2.36, datetime.datetime(2022, 10, 28, 7, 51, 6, 790000)]
[(-97.6485, 36.92433333), 36.92433333, -97.6485, 4.55, 2.35, datetime.datetime(2022, 10, 27, 19, 41, 58, 229000)]
[(-97.78266667, 36.85433333), 36.85433333, -97.78266667, 7.86, 2.42, datetime.datetime(2022, 10, 27, 16, 56, 11, 800000)]
[(-97.37833333, 34.54816667), 34.54816667, -97.37833333, 4.12, 2.22, datetime.datetime(2022, 10, 27, 16, 37, 18, 480000)]
[(-97.7895, 36.851), 36.851, -97.7895, 5.41, 2.13, datetime.datetime(2022, 10, 27, 3, 41, 32, 280000)]
[(-97.37633333, 34.54216667), 34.54216667, -97.37633333, 4.19, 2.16, datetime.datetime(2022, 10, 26, 1, 13, 21, 589000)]
[(-101.798753, 35.16448975), 35.16448975, -101.798753, 4.377400716, 2.2, datetime.datetime(2022, 10, 26, 1, 6, 18, 256000)]
[(-101.810021, 35.17547607), 35.17547607, -101.810021, 3.350301107, 2.4, datetime.datetime(2022, 10, 25, 9, 9, 6, 101000)]
[(-97.874, 36.83783333), 36.83783333, -97.874, 7.75, 2.08, datetime.datetime(2022, 10, 24, 15, 53, 2, 379000)]
[(-98.8045, 36.96633333), 36.96633333, -98.8045, 7.34, 2.05, datetime.datetime(2022, 10, 24, 11, 37, 18, 489000)]
[(-97.87183333, 36.83533333), 36.83533333, -97.87183333, 7.18, 2.93, datetime.datetime(2022, 10, 22, 19, 41, 33, 840000)]
[(-97.89, 35.9405), 35.9405, -97.89, 6.42, 2.59, datetime.datetime(2022, 10, 18, 21, 41, 15, 29000)]
[(-97.89133333, 35.938), 35.938, -97.89133333, 5.44, 2.04, datetime.datetime(2022, 10, 14, 20, 46, 52, 170000)]
[(-97.8745, 35.949), 35.949, -97.8745, 6.44, 2.01, datetime.datetime(2022, 10, 14, 17, 20, 7, 610000)]
[(-97.874, 35.95033333), 35.95033333, -97.874, 7.32, 2.74, datetime.datetime(2022, 10, 14, 14, 54, 51, 429000)]
[(-97.49333333, 36.98083333), 36.98083333, -97.49333333, 6.18, 2.67, datetime.datetime(2022, 10, 13, 17, 21, 45, 60000)]
[(-97.8785, 35.94816667), 35.94816667, -97.8785, 6.42, 2.38, datetime.datetime(2022, 10, 13, 15, 0, 0, 850000)]
[(-97.87283333, 35.95316667), 35.95316667, -97.87283333, 5.7, 2.61, datetime.datetime(2022, 10, 13, 14, 55, 16, 519000)]
[(-97.878, 35.9465), 35.9465, -97.878, 5.45, 2.24, datetime.datetime(2022, 10, 13, 14, 55, 6, 820000)]
[(-97.87416667, 35.94733333), 35.94733333, -97.87416667, 6.53, 2.4, datetime.datetime(2022, 10, 13, 3, 34, 16, 10000)]
[(-97.87333333, 35.94766667), 35.94766667, -97.87333333, 6.33, 2.47, datetime.datetime(2022, 10, 13, 3, 31, 40, 100000)]
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[(-101.742, 35.277), 35.277, -101.742, 5.0, 3.3, datetime.datetime(2003, 9, 24, 15, 2, 9, 90000)]
[(-97.695, 33.892), 33.892, -97.695, 5.0, 2.9, datetime.datetime(2003, 4, 7, 10, 2, 12, 510000)]
[(-96.079, 34.274), 34.274, -96.079, 5.0, 3.4, datetime.datetime(2002, 10, 20, 2, 18, 13)]
[(-97.619, 34.025), 34.025, -97.619, 5.0, 3.3, datetime.datetime(2002, 5, 31, 9, 57, 10, 20000)]
[(-101.824, 35.359), 35.359, -101.824, 5.0, 2.8, datetime.datetime(2002, 3, 31, 2, 54, 8, 130000)]
[(-97.63, 34.27), 34.27, -97.63, 5.0, 2.2, datetime.datetime(2002, 3, 12, 10, 52, 4, 590000)]
[(-98.361, 34.727), 34.727, -98.361, 5.0, 3.8, datetime.datetime(2002, 2, 8, 16, 7, 13, 600000)]
[(-97.53, 34.0), 34.0, -97.53, 5.0, 2.6, datetime.datetime(2002, 1, 25, 10, 31, 27, 600000)]
[(-101.818, 35.341), 35.341, -101.818, 5.0, 2.5, datetime.datetime(2002, 1, 16, 15, 25, 32, 450000)]
[(-101.8, 35.4), 35.4, -101.8, 5.0, 3.9, datetime.datetime(2000, 12, 16, 22, 8, 54)]
[(-97.98, 35.393), 35.393, -97.98, 5.0, 2.6, datetime.datetime(2000, 10, 8, 10, 16, 23, 780000)]
[(-101.814, 35.39), 35.39, -101.814, 5.0, 3.9, datetime.datetime(2000, 8, 17, 1, 8, 5, 450000)]
[(-101.812, 35.392), 35.392, -101.812, 5.0, 3.0, datetime.datetime(2000, 8, 10, 13, 39, 50)]
[(-101.812, 35.392), 35.392, -101.812, 5.0, 3.0, datetime.datetime(2000, 8, 7, 21, 36, 21)]
[(-101.812, 35.392), 35.392, -101.812, 5.0, 3.0, datetime.datetime(2000, 8, 7, 18, 34, 9)]
[(-101.812, 35.392), 35.392, -101.812, 5.0, 3.3, datetime.datetime(2000, 8, 7, 17, 19, 8)]
[(-101.9, 35.2), 35.2, -101.9, 5.0, 2.7, datetime.datetime(2000, 8, 2, 12, 21, 30, 60000)]
[(-99.659, 36.846), 36.846, -99.659, 26.1, 3.0, datetime.datetime(1999, 10, 25, 23, 19, 58, 370000)]
[(-97.6, 36.8), 36.8, -97.6, 5.0, 3.5, datetime.datetime(1998, 10, 30, 17, 41, 22, 200000)]
[(-97.589, 34.719), 34.719, -97.589, 5.0, 3.2, datetime.datetime(1998, 7, 7, 18, 44, 44, 460000)]
[(-98.416, 34.782), 34.782, -98.416, 5.0, 3.9, datetime.datetime(1998, 4, 28, 14, 13, 1, 680000)]
[(-102.383, 35.453), 35.453, -102.383, 5.0, 3.2, datetime.datetime(1998, 4, 27, 15, 22, 46, 250000)]
[(-96.435, 34.66), 34.66, -96.435, 5.0, 4.5, datetime.datetime(1997, 9, 6, 23, 38, 0, 910000)]
[(-97.499, 34.72), 34.72, -97.499, 5.0, 2.5, datetime.datetime(1997, 3, 11, 13, 30, 30, 920000)]
[(-100.569, 34.973), 34.973, -100.569, 5.0, 3.2, datetime.datetime(1997, 2, 15, 9, 8, 55, 460000)]
[(-100.89, 34.947), 34.947, -100.89, 5.0, 3.0, datetime.datetime(1997, 2, 12, 23, 53, 10, 770000)]
[(-97.647, 35.081), 35.081, -97.647, 5.0, 2.5, datetime.datetime(1996, 12, 19, 16, 29, 57, 720000)]
[(-100.504, 35.04), 35.04, -100.504, 5.0, 3.0, datetime.datetime(1996, 11, 23, 10, 54, 18, 500000)]
[(-102.601, 35.61), 35.61, -102.601, 5.0, 3.5, datetime.datetime(1996, 3, 25, 6, 43, 46, 860000)]
[(-99.337, 35.061), 35.061, -99.337, 5.0, 2.9, datetime.datetime(1995, 12, 1, 14, 37, 40, 440000)]
[(-98.69, 36.87), 36.87, -98.69, 5.0, 4.1, datetime.datetime(1995, 9, 15, 0, 31, 33, 260000)]
[(-102.378, 35.436), 35.436, -102.378, 5.0, 2.8, datetime.datetime(1995, 6, 28, 3, 38, 57, 390000)]
[(-96.732, 34.287), 34.287, -96.732, 5.0, 3.0, datetime.datetime(1995, 6, 1, 4, 49, 29, 320000)]
[(-99.028, 35.2), 35.2, -99.028, 5.0, 3.0, datetime.datetime(1995, 4, 5, 5, 31, 16, 230000)]
[(-99.6, 36.9), 36.9, -99.6, 5.0, 2.8, datetime.datetime(1995, 3, 23, 11, 10, 12, 310000)]
[(-97.596, 34.774), 34.774, -97.596, 5.0, 4.2, datetime.datetime(1995, 1, 18, 15, 51, 39, 420000)]
[(-97.557, 34.676), 34.676, -97.557, 5.0, 2.8, datetime.datetime(1994, 7, 4, 7, 28, 27, 800000)]
[(-98.09, 36.25), 36.25, -98.09, 5.0, 3.0, datetime.datetime(1994, 4, 29, 3, 28, 58, 680000)]
[(-102.981, 35.868), 35.868, -102.981, 5.0, 3.3, datetime.datetime(1993, 9, 29, 2, 1, 19, 60000)]
[(-95.93, 35.21), 35.21, -95.93, 5.0, 2.7, datetime.datetime(1993, 3, 11, 1, 15, 1, 500000)]
[(-97.1, 34.04), 34.04, -97.1, 5.0, 2.0, datetime.datetime(1993, 1, 30, 4, 42, 53, 340000)]
[(-98.275, 36.595), 36.595, -98.275, 5.0, 3.1, datetime.datetime(1993, 1, 14, 17, 6, 10, 450000)]
[(-97.581, 34.744), 34.744, -97.581, 5.0, 3.6, datetime.datetime(1992, 12, 17, 7, 18, 4, 270000)]
[(-97.6, 34.756), 34.756, -97.6, 5.0, 2.6, datetime.datetime(1992, 12, 17, 4, 1, 17, 570000)]
[(-97.67, 34.83), 34.83, -97.67, 5.0, 2.3, datetime.datetime(1992, 11, 23, 11, 56, 9, 900000)]
[(-97.68, 34.83), 34.83, -97.68, 5.0, 2.2, datetime.datetime(1992, 11, 21, 2, 21, 43, 200000)]
[(-97.55, 35.2), 35.2, -97.55, 5.0, 2.0, datetime.datetime(1992, 11, 18, 21, 40, 48, 200000)]
[(-97.503, 36.402), 36.402, -97.503, 5.0, 2.8, datetime.datetime(1992, 10, 5, 4, 44, 8, 600000)]
[(-97.453, 34.982), 34.982, -97.453, 5.0, 2.9, datetime.datetime(1992, 8, 10, 20, 3, 4, 200000)]
[(-96.542, 34.618), 34.618, -96.542, 5.0, 2.2, datetime.datetime(1992, 8, 10, 11, 21, 23, 100000)]
[(-96.491, 34.77), 34.77, -96.491, 5.0, 2.2, datetime.datetime(1992, 8, 9, 21, 5, 52, 100000)]
[(-96.421, 35.261), 35.261, -96.421, 5.0, 2.7, datetime.datetime(1992, 6, 30, 1, 25, 49, 300000)]
[(-97.667, 34.808), 34.808, -97.667, 5.0, 2.2, datetime.datetime(1992, 3, 20, 12, 39, 35)]
[(-97.3, 36.378), 36.378, -97.3, 5.0, 3.0, datetime.datetime(1991, 1, 24, 5, 0, 26, 900000)]
[(-97.59, 34.76), 34.76, -97.59, 5.0, 3.9, datetime.datetime(1990, 11, 15, 11, 44, 41, 400000)]
[(-95.53, 34.8), 34.8, -95.53, 5.0, 2.5, datetime.datetime(1990, 9, 16, 21, 13, 32, 400000)]
[(-102.106, 35.413), 35.413, -102.106, 5.0, 2.7, datetime.datetime(1990, 7, 1, 13, 6, 34, 790000)]
[(-98.827, 35.629), 35.629, -98.827, 5.0, 2.5, datetime.datetime(1990, 2, 7, 12, 2, 14, 100000)]
[(-98.876, 36.434), 36.434, -98.876, 5.0, 3.1, datetime.datetime(1989, 7, 20, 6, 7, 50, 420000)]
[(-98.981, 36.405), 36.405, -98.981, 5.0, 2.7, datetime.datetime(1989, 7, 20, 2, 49, 48, 550000)]
[(-96.464, 35.324), 35.324, -96.464, 5.0, 2.1, datetime.datetime(1989, 2, 20, 11, 59, 18)]
[(-96.831, 34.386), 34.386, -96.831, 5.0, 2.0, datetime.datetime(1989, 2, 7, 22, 22, 46, 700000)]
[(-96.144, 34.091), 34.091, -96.144, 5.0, 2.4, datetime.datetime(1988, 10, 13, 14, 42, 6, 800000)]
[(-98.075, 35.883), 35.883, -98.075, 5.0, 2.1, datetime.datetime(1988, 10, 12, 10, 11, 46)]
[(-98.71, 35.91), 35.91, -98.71, 5.0, 2.3, datetime.datetime(1988, 7, 5, 23, 22, 40)]
[(-96.263, 34.507), 34.507, -96.263, 5.0, 2.1, datetime.datetime(1988, 6, 21, 23, 12, 45, 600000)]
[(-99.657, 33.975), 33.975, -99.657, 5.0, 2.3, datetime.datetime(1988, 6, 19, 22, 41, 16, 900000)]
[(-98.71, 34.034), 34.034, -98.71, 5.0, 2.0, datetime.datetime(1988, 6, 18, 7, 39, 54, 370000)]
[(-95.19, 34.743), 34.743, -95.19, 5.0, 2.1, datetime.datetime(1988, 6, 5, 2, 56, 55, 500000)]
[(-98.543, 36.31), 36.31, -98.543, 5.0, 2.3, datetime.datetime(1988, 3, 30, 15, 46, 55, 300000)]
[(-96.573, 35.414), 35.414, -96.573, 5.0, 2.3, datetime.datetime(1988, 3, 24, 2, 25, 47, 900000)]
[(-95.512, 34.877), 34.877, -95.512, 5.0, 2.1, datetime.datetime(1987, 12, 16, 7, 4, 58, 600000)]
[(-98.03, 36.056), 36.056, -98.03, 5.0, 2.5, datetime.datetime(1987, 12, 8, 1, 45, 47, 500000)]
[(-98.024, 36.055), 36.055, -98.024, 5.0, 3.7, datetime.datetime(1987, 12, 8, 1, 42, 40, 300000)]
[(-97.348, 34.581), 34.581, -97.348, 5.0, 2.0, datetime.datetime(1987, 12, 7, 0, 44, 1)]
[(-97.394, 34.664), 34.664, -97.394, 5.0, 2.6, datetime.datetime(1987, 12, 6, 17, 43, 48, 200000)]
[(-97.38, 34.615), 34.615, -97.38, 5.0, 2.9, datetime.datetime(1987, 6, 1, 17, 44, 33, 200000)]
[(-97.749, 35.455), 35.455, -97.749, 5.0, 2.1, datetime.datetime(1987, 5, 15, 8, 29, 7, 500000)]
[(-96.331, 34.79), 34.79, -96.331, 5.0, 2.8, datetime.datetime(1987, 3, 14, 4, 43, 3, 500000)]
[(-97.488, 34.846), 34.846, -97.488, 5.0, 2.1, datetime.datetime(1987, 2, 19, 5, 50, 11, 500000)]
[(-98.097, 35.828), 35.828, -98.097, 5.0, 3.1, datetime.datetime(1987, 1, 24, 16, 8, 17)]
[(-97.517, 35.051), 35.051, -97.517, 5.0, 2.3, datetime.datetime(1987, 1, 17, 4, 13, 53, 800000)]
[(-97.425, 34.552), 34.552, -97.425, 5.0, 2.3, datetime.datetime(1987, 1, 10, 3, 21, 50)]
[(-100.35, 36.212), 36.212, -100.35, 5.0, 2.0, datetime.datetime(1986, 12, 22, 0, 17, 25)]
[(-96.676, 35.142), 35.142, -96.676, 5.0, 2.8, datetime.datetime(1986, 12, 21, 17, 32, 58, 100000)]
[(-101.605, 35.09), 35.09, -101.605, 5.0, 2.5, datetime.datetime(1986, 12, 11, 1, 23, 0, 600000)]
[(-97.328, 35.766), 35.766, -97.328, 5.0, 2.4, datetime.datetime(1986, 12, 8, 17, 50, 11, 800000)]
[(-101.561, 36.993), 36.993, -101.561, 5.0, 2.4, datetime.datetime(1986, 11, 5, 13, 34, 46, 200000)]
[(-96.503, 34.477), 34.477, -96.503, 5.0, 2.6, datetime.datetime(1986, 9, 4, 17, 33, 17, 400000)]
[(-96.483, 34.684), 34.684, -96.483, 5.0, 2.1, datetime.datetime(1986, 9, 2, 13, 19, 59)]
[(-96.62, 34.591), 34.591, -96.62, 5.0, 2.3, datetime.datetime(1986, 7, 26, 4, 17, 23, 800000)]
[(-96.752, 34.706), 34.706, -96.752, 5.0, 2.3, datetime.datetime(1986, 6, 30, 19, 55, 51, 200000)]
[(-96.897, 35.656), 35.656, -96.897, 5.0, 2.0, datetime.datetime(1986, 6, 1, 19, 52, 38, 200000)]
[(-94.877, 36.23), 36.23, -94.877, 5.0, 2.2, datetime.datetime(1986, 5, 25, 10, 27, 44, 800000)]
[(-97.36, 34.931), 34.931, -97.36, 5.0, 2.2, datetime.datetime(1986, 4, 30, 3, 36, 10, 700000)]
[(-102.514, 35.308), 35.308, -102.514, 5.0, 3.1, datetime.datetime(1986, 3, 3, 11, 45, 17, 480000)]
[(-97.479, 34.692), 34.692, -97.479, 5.0, 2.3, datetime.datetime(1986, 2, 24, 23, 52, 22, 30000)]
[(-97.484, 34.76), 34.76, -97.484, 5.0, 2.7, datetime.datetime(1985, 12, 31, 18, 27, 25, 820000)]
[(-97.788, 34.224), 34.224, -97.788, 5.0, 2.0, datetime.datetime(1985, 11, 7, 8, 35, 33, 320000)]
[(-95.164, 34.629), 34.629, -95.164, 5.0, 2.9, datetime.datetime(1985, 9, 23, 1, 3, 43, 60000)]
[(-97.414, 34.711), 34.711, -97.414, 5.0, 2.9, datetime.datetime(1984, 11, 20, 10, 57, 32, 20000)]
[(-102.228, 35.067), 35.067, -102.228, 5.0, 3.1, datetime.datetime(1984, 5, 21, 13, 31, 13, 540000)]
[(-96.3, 35.51), 35.51, -96.3, 5.0, 2.6, datetime.datetime(1984, 3, 3, 11, 42, 2, 400000)]
[(-97.394, 34.657), 34.657, -97.394, 5.0, 3.2, datetime.datetime(1984, 2, 3, 4, 38, 27, 110000)]
[(-95.58, 36.16), 36.16, -95.58, 5.0, 2.6, datetime.datetime(1984, 1, 6, 17, 14, 49, 800000)]
[(-102.321, 35.448), 35.448, -102.321, 5.0, 3.4, datetime.datetime(1983, 4, 3, 4, 55, 21, 250000)]
[(-100.196, 36.791), 36.791, -100.196, 5.0, 2.7, datetime.datetime(1983, 3, 11, 16, 50, 45, 310000)]
[(-102.571, 36.102), 36.102, -102.571, 5.0, 3.9, datetime.datetime(1982, 10, 14, 12, 52, 46, 300000)]
[(-96.38, 34.07), 34.07, -96.38, 5.0, 3.0, datetime.datetime(1982, 5, 3, 7, 54, 50)]
[(-97.677, 34.884), 34.884, -97.677, 5.0, 3.5, datetime.datetime(1981, 7, 11, 21, 9, 22, 540000)]
[(-97.777, 35.472), 35.472, -97.777, 8.0, 3.0, datetime.datetime(1980, 11, 2, 10, 0, 49, 300000)]
[(-101.082, 35.513), 35.513, -101.082, 5.0, 3.4, datetime.datetime(1980, 6, 9, 22, 37, 9, 900000)]
[(-101.076, 35.281), 35.281, -101.076, 5.0, 2.9, datetime.datetime(1980, 2, 21, 20, 42, 0, 900000)]
[(-99.518, 35.219), 35.219, -99.518, 5.0, 3.4, datetime.datetime(1979, 9, 13, 0, 49, 19, 800000)]
[(-99.812, 35.187), 35.187, -99.812, 5.0, 3.0, datetime.datetime(1979, 6, 7, 7, 39, 35, 600000)]
[(-99.841, 36.134), 36.134, -99.841, 5.0, 3.5, datetime.datetime(1976, 4, 19, 4, 42, 42, 200000)]
[(-99.875, 36.107), 36.107, -99.875, 5.0, 3.4, datetime.datetime(1976, 4, 16, 18, 59, 44, 200000)]
[(-102.083, 36.615), 36.615, -102.083, 5.0, 2.7, datetime.datetime(1976, 3, 30, 9, 27, 1)]
[(-97.347, 34.521), 34.521, -97.347, 5.0, 3.5, datetime.datetime(1975, 11, 29, 14, 29, 40, 900000)]
[(-97.406, 34.816), 34.816, -97.406, 20.0, 3.2, datetime.datetime(1975, 10, 12, 2, 58, 11, 200000)]
[(-97.369, 34.139), 34.139, -97.369, 5.0, 3.4, datetime.datetime(1975, 9, 13, 1, 25, 2, 800000)]
[(-97.48, 35.33), 35.33, -97.48, 10.0, 2.6, datetime.datetime(1974, 12, 16, 2, 30, 21, 400000)]
[(-100.693, 36.5), 36.5, -100.693, 24.0, 4.5, datetime.datetime(1974, 2, 15, 13, 33, 49, 200000)]

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_26128\334631549.py in <cell line: 5>()
     15         lon=float(fields[3])
     16 
---> 17         depth=float(fields[4])
     18         mag=float(fields[10])
     19         XY=(float(lon), float(lat))

ValueError: could not convert string to float: ''

Plot the eqathqakes using matplotlib

# import the matplolib module

import matplotlib.pyplot as plt


# Initialize empty lists to store coordinates and magnitudes
x_coords = []
y_coords = []
magnitudes = []

# Create a cursor to iterate over the features
with arcpy.da.SearchCursor(eqFC, ["SHAPE@XY", "Magnitude"]) as cursor:
    for row in cursor:
        # Extract x, y coordinates and magnitude
        x, y = row[0]
        magnitude = row[1]
        
        # Append to lists
        x_coords.append(x)
        y_coords.append(y)
        magnitudes.append(magnitude)

# Plot the earthquake color coded by magnitud value
plt.figure(figsize=(10, 6))
plt.scatter(x_coords, y_coords, s=[mag*5 for mag in magnitudes], c=magnitudes, cmap='viridis', alpha=0.5)  
tit='Earthquakes in %s from 1960-2023' %state_name 
plt.title(tit,fontsize=14,fontweight='bold')
plt.xlabel('Latitude',fontsize=12,fontweight='bold')
plt.ylabel('Longitude',fontsize=12,fontweight='bold')
plt.grid(True,linestyle='--')

# Add a colorbar
cbar = plt.colorbar()
cbar.set_label('Magnitude')
plt.tight_layout()

#save it as png files
outpng= str(state_name) + '_earthquakes.png'
outpng_path=os.path.join(out_maps,outpng)

plt.savefig(outpng_path, dpi=300, bbox_inches='tight')
plt.show()

This plot shows magnitdue 2+ earhquakes in Oklahoma (from 1960 to 2023), color coded by magnitudes

Form this plot, we can see a large number of low magnitude low magnitude earthquakes are concentrated around the northern-central and south-southeast part of Oklahoma.

Plot cumulative number of M2.0+ earthquakes

# make a new table containing cumulative number of eq by year
from collections import defaultdict

# Initialize a defaultdict to store the count of earthquakes by year
earthquake_counts = defaultdict(int)

# Read the "|" delimited text file
with open(outfile_path, 'r') as file:
    # Skip the header line
    next(file)
    for line in file:
        fields = line.strip().split('|')
        if len(fields) >= 2:
            # Extract the year from the date string
            year = fields[1][:4]
            # Increment the count for the current year
            earthquake_counts[year] += 1

# Create a new text file to store the aggregated earthquake counts
cumeq_path=os.path.join(project_data_path,"cum_eq_counts.txt")
with open(cumeq_path, 'w') as output_file:
    cumulative_count = 0
    for year in sorted(earthquake_counts.keys()):
        cumulative_count += earthquake_counts[year]
        output_file.write(f"{year}|{earthquake_counts[year]}|{cumulative_count}\n")

# plot the cumulative curve
import matplotlib.pyplot as plt
cumeq_path=os.path.join(project_data_path,"cum_eq_counts.txt")
# Initialize lists to store years and cumulative counts
years = []
cumulative_counts = []

# Initialize cumulative count
cumulative_count = 0

# Open the file and iterate through each line
with open(cumeq_path, 'r') as file:
    for line in file:
        # Split the line using "|" delimiter
        fields = line.split("|")
        
        # Extract year and count
        year = fields[0]
        c_count = int(fields[2])  # Convert count to integer
        
        # Accumulate count
        #cumulative_count += count
        
        # Append to lists
        years.append(year)
        cumulative_counts.append(c_count)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(years, cumulative_counts, marker='o', linestyle='-',markersize=4)
plt.title('Cumulative 2.0+ Earthquakes in ' + str(state_name), fontsize=16, fontweight='bold')
plt.xlabel('Year', fontsize=14, fontweight='bold')
plt.ylabel('Cumulative Count', fontsize=14, fontweight='bold')
plt.xticks(years[::5], rotation=45) 
plt.grid(False)
plt.tight_layout()
#plt.show()

# save the plot as png file 
png_name='cumulative_eq_count_%s.png' %state_name
outpng_path=os.path.join(out_maps,png_name)

plt.savefig(outpng_path,dpi=300)  
plt.show()

This plot shows the sudden jump the seismicity in Okalhama from 2011 when the injection wells were reached its peak number

Analyze the temporal seimicity patten change due to the injection

# This codeblock plot the earthquakes befter and after 2011 to see if we can see any pattern

import arcpy
import matplotlib.pyplot as plt

# Set the workspace and feature class path

earthquake_fc = eqFC # Change to your earthquake feature class name

# Define the SQL queries to filter earthquakes before and after 2009
query_before_2009 = "DateTime >= timestamp '2011-01-01 00:00:00'"
query_after_2009 = "DateTime < timestamp '2011-01-01 00:00:00'"

# Create two separate feature layers based on the SQL queries
arcpy.MakeFeatureLayer_management(earthquake_fc, "earthquakes_before_2009", query_before_2009)
arcpy.MakeFeatureLayer_management(earthquake_fc, "earthquakes_after_2009", query_after_2009)

# Get earthquake locations and plot them
fig, axes = plt.subplots(1, 2, figsize=(12, 6))

# Plot earthquakes before 2009
with arcpy.da.SearchCursor("earthquakes_before_2009", ["SHAPE@X", "SHAPE@Y"]) as cursor:
    for row in cursor:
        axes[0].plot(row[0], row[1], 'ro', markersize=3, alpha=0.5)

axes[0].set_title('Earthquakes After 2009', fontsize=14,fontweight='bold')
axes[0].set_xlabel('Longitude', fontsize=12,fontweight='bold')
axes[0].set_ylabel('Latitude', fontsize=12,fontweight='bold')
axes[0].grid(linestyle='--')

# Plot earthquakes after 2009
with arcpy.da.SearchCursor("earthquakes_after_2009", ["SHAPE@X", "SHAPE@Y"]) as cursor:
    for row in cursor:
        axes[1].plot(row[0], row[1], 'bo', markersize=2.5, alpha=0.5)

axes[1].set_title('Earthquakes Before 2009', fontsize=14,fontweight='bold')
axes[1].set_xlabel('Longitude', fontsize=12,fontweight='bold')
axes[1].set_ylabel('Latitude', fontsize=12,fontweight='bold')
axes[1].grid(linestyle='--')

# Adjust layout
plt.tight_layout()

#save it as png files
outpng= str(state_name) + '_eq_comparison.png'
outpng_path=os.path.join(out_maps,outpng)
plt.savefig(outpng_path, dpi=300, bbox_inches='tight')


# Show the plot
plt.show()

This fiugre shows the earthquake location before and after 2011, when we see the onset of the higher seismicty in Oklahoma.
We also see the change of seismicity pattens after 2011, where a huge swarm of earthquake are concentrated in the northern part of the state.

Undertand the relationship between earthquakes and active faults

In this section, I'll explore whether we can discern the spatial relationship among the active faults and earthquakes in this region.

# This codeblock create a new featureclass form US states that only have the satae of interest(Oklahoma)

# Input feature class containing US states
inputFC_path= os.path.join(GDBpath,stateFc)

# Output feature class name and path containing only Oklahoma
outFC_state_path = os.path.join(GDBpath,state_name)

# SQL expression to select only Oklahoma
sql_expression = "STATE_NAME = '%s'" %state_name

# Execute Select_analysis to create a new feature class
arcpy.Select_analysis(inputFC_path, outFC_state_path,sql_expression)

# clip the fault using state bounding geometry of the state 

# Input feature class to be clipped
faultFC = "US_faults"
outFC_state_path=os.path.join(GDBpath,state_name)
# Output clipped feature class
state_fault='%s_faults' %state_name

clipFC_path=os.path.join(GDBpath,state_fault)

# Clip the input feature class using the OK state feature class
arcpy.analysis.Clip(faultFC, outFC_state_path, clipFC_path)

# create earthquake featue class 

arcpy.env.workspace=GDBpath
# Output kernel density raster
eq_density = "%s_eq_density" %state_name

binSize = "18 Kilometers"
neighborhoodSize = "36 Kilometers"
wt="UNIFORM"

arcpy.gapro.CalculateDensity(eqFC, eq_density, "SQUARE", binSize, wt, neighborhoodSize)

Make a denstiy map of the eatquakes

• In this step, I set up the project file and map layouts to for better plotting and visualizaion

projectLocation=os.path.join(BaseDirectory,"project_mohimanul.aprx")
#projectLocation=r"C:\Users\tnm\Documents\ArcGIS\Final_project\project_mohimanul_3.aprx"
# reference the project
aprxProject = arcpy.mp.ArcGISProject(projectLocation)

#Maps = aprxProject.listMaps()

mainMap = aprxProject.listMaps('Map_Density')[0]

Layouts = aprxProject.listLayouts('EQ_density_with_fault')[0]

print(mainMap)

#print(projectLayout)
Layers =  mainMap.listLayers()


#eq_density layer
eq_density_lyr = mainMap.listLayers(eq_density)[0]
print(eq_density_lyr)
# state layer
state_lyr = mainMap.listLayers(state_name)[0]
print(state_lyr)
# fault layer 
fault_lyr = mainMap.listLayers(state_fault)[0]
print(fault_lyr)

<arcpy._mp.Map object at 0x0000020D0E0E6940>
Oklahoma_eq_density
Oklahoma
Oklahoma_faults

# list elements
#rojectMapFrame = Layouts.listElements('MAPFRAME_ELEMENT', "mainMap Map Frame")[0]

<arcpy._mp.MapFrame object at 0x000001E024918FD0>

## change the map title
mapTitle_text="%s Earthquake Density (km^2) with active faults" %state_name

elm =Layouts.listElements("TEXT_ELEMENT")
for e in elm:
    if (e.name == "Title"):
        e.text = mapTitle_text

        
# change the map extent

projectMapFrame = Layouts.listElements('MAPFRAME_ELEMENT',"Map Frame")[0]


desc=arcpy.Describe(eq_density)
extent=desc.extent
#print(projectMapFrame)

# change extent
projectMapFrame.panToExtent(extent)

# upadate the connection properties for adding faults and eq density 

# for fault
fault_cp = fault_lyr.connectionProperties
existingDataset = fault_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': state_fault})


# for eq density 
eq_density_cp = eq_density_lyr.connectionProperties
existingDataset = eq_density_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': eq_density})


# for state bounding polygon
state_cp = state_lyr.connectionProperties
existingDataset = state_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': state_name})



# save the project.
aprxProject.save()

Oklahoma_faults
Oklahoma_eq_density
Oklahoma

# save the map as png
aprxProject = arcpy.mp.ArcGISProject(projectLocation)

mainMap = aprxProject.listMaps('Map_Density')[0]
Layouts = aprxProject.listLayouts('EQ_density_with_fault')[0]

outPNG='%s_Eq_density_fault' %state_name
outPNG_path = os.path.join(out_maps, outPNG)
#Export the map as a .png
Layouts.exportToPNG(outPNG_path)

'c:\\Users\\tnm\\OneDrive - University of Missouri\\GIS2\\IslamGISNotebooks\\FinalProject\\Figures\\Oklahoma_Eq_density_fault_2.png'

The above figure shows the eathquke density (per km2) and active faults in Oklahoma that demonstrate geological evidence of coseismic surface deformation in large earthquakes during the past 1.6 million years (Ma)

The number of active faults in Oklahoma is notably limited, confined primarily to the southwest region of the state. In stark contrast, earthquake occurrences are concentrated in disparate areas. This discrepancy strongly suggests a connection between recent earthquakes and injection/fracking activities. As previously noted, saltwater injection and fracking operations lead to a increase in pore pressure, thereby triggering activity along pre-existing fault lines or weaknesses. When pressure surpasses the threshold limit, these faults fail, resulting in the sudden release of energy felt as earthquakes..

Spatial relationshiop between the eartquakes and injection activity

In part, I want look wherther we can find the relationship between the eartquakes and areas affected by injection well. Since most of wells are horizontal in nature and ofter goes thousandes of meters away form the drilling point, I will make a 5 km buffer zone aroud the wells.

# create a buffer zone aroud the wells 
wellFC= '%s_class_II_injection_wells' %state_name_abr

# Output buffer feature class
wellFC_buf = "well_buffer"

# Distance for buffer (5 kilometers in this case)
buffer_distance = "5 Kilometers"

# Create buffer
arcpy.Buffer_analysis(in_features=wellFC, out_feature_class=wellFC_buf,
                      buffer_distance_or_field=buffer_distance,dissolve_option='ALL')

Update the maps layouts and replot the map

# update the maps layouts and replot the map

projectLocation=os.path.join(BaseDirectory,"project_mohimanul.aprx")
#projectLocation=r"C:\Users\tnm\Documents\ArcGIS\Final_project\project_mohimanul_4.aprx"
# reference the project
aprxProject = arcpy.mp.ArcGISProject(projectLocation)



mainMap = aprxProject.listMaps('Map_Wells')[0]

Layouts = aprxProject.listLayouts('Wells_EQ_density')[0]

print(mainMap)

#print(projectLayout)
Layers =  mainMap.listLayers()


#eq_density layer
eq_density_lyr = mainMap.listLayers(eq_density)[0]
print(eq_density_lyr)
# state layer
state_lyr = mainMap.listLayers(state_name)[0]
print(state_lyr)
# fault layer 
well_lyr = mainMap.listLayers(wellFC_buf)[0]
print(well_lyr)

<arcpy._mp.Map object at 0x0000020D0E1495E0>
Oklahoma_eq_density
Oklahoma
well_buffer

Maps = aprxProject.listMaps()
mainMap = aprxProject.listMaps('Map_Wells')[0]
#for m in Maps:
#    print(m.name)
    
#Layouts = aprxProject.listLayouts()
Layers=mainMap.listLayers()
for l in Layers:
    print(l.name)
    
elm =Layouts.listElements("TEXT_ELEMENT")
for e in elm:
    if (e.name == "Title"):
        print(e.text)

well_buffer
Oklahoma
Oklahoma_faults
Oklahoma_eq_density
World Topographic Map
World Hillshade
Oklahoma injection well areas with EQ density

## change the map title
mapTitle_text="%s injection well areas with EQ density" %state_name

elm =Layouts.listElements("TEXT_ELEMENT")
for e in elm:
    if (e.name == "Title"):
        e.text = mapTitle_text

        
# change the map extent

projectMapFrame = Layouts.listElements('MAPFRAME_ELEMENT',"Map Frame")[0]


desc=arcpy.Describe(eq_density)
extent=desc.extent
#print(projectMapFrame)

# change extent
projectMapFrame.panToExtent(extent)

# upadate the connection properties for adding faults and eq density 

# for well buffer
well_cp = well_lyr.connectionProperties
existingDataset = well_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': wellFC_buf})


# for eq density 
eq_density_cp = eq_density_lyr.connectionProperties
existingDataset = eq_density_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': eq_density})


# for state bounding polygon
state_cp = state_lyr.connectionProperties
existingDataset = state_cp['dataset']
print(existingDataset)
aprxProject.updateConnectionProperties({'dataset':existingDataset}, {'dataset': state_name})



# save the project.
aprxProject.save()

well_buffer
Oklahoma_eq_density
Oklahoma

# save the map as png
aprxProject = arcpy.mp.ArcGISProject(projectLocation)

mainMap = aprxProject.listMaps('Map_Wells')[0]
Layouts = aprxProject.listLayouts('Wells_EQ_density')[0]

outPNG='%s_Eq_density_wells_2' %state_name
outPNG_path = os.path.join(out_maps, outPNG)
#Export the map as a .png
Layouts.exportToPNG(outPNG_path)

'c:\\Users\\tnm\\OneDrive - University of Missouri\\GIS2\\IslamGISNotebooks\\FinalProject\\Figures\\Oklahoma_Eq_density_wells_2.png'

From this figure, it's evident that the 5 km buffer around the injection wells is primarily concentrated in the north-central region of the state, coinciding with areas exhibiting higher earthquake density. This spatial pattern aligns with the elevated injection volumes in the northern sector (MaGarr, 2014; Walsh & Zoback, 2015), contributing to increased pore pressure within the reservoir. Consequently, this heightened pressure likely instigates frequent slip events within the geological weak zones.

Conclusion

This study showcases the application of geospatial analysis using publicly available datasets. By employing straightforward GIS analysis techniques, we can readily identify well-known phenomena such as induced seismicity.

Here, I have analyzed With the aid of scripting, this can be done In the context of triggered seismicity, the largest earthquake that might be triggered is determined by preexisting geologic conditions, not the magnitude of the perturbation of pore pressure.

References

W.-Y. Kim, Induced seismicity associated with fluid injection into a deep well in Youngstown, Ohio. J. Geophys. Res. 118, 3506–3518 (2013).
C. Frohlich, W. Ellsworth, W. A. Brown, M. Brunt, J. Luetgert, T. MacDonald, S. Walter, The 17 May 2012 M4.8 earthquake near Timpson, East Texas: An event possibly triggered by fluid injection. J. Geophys. Res. 119, 581–593 (2014).
S. Horton, Disposal of hydrofracking waste fluid by injection into subsurface aquifers triggers earthquake swarm in central Arkansas with potential for damaging earthquake. Seismol. Res. Lett. 83, 250–260 (2012)
A McGarr, B. Bekins, N. Burkardt, J. Dewey, P. Earle, W. Ellsworth, S. Ge, S. Hickman, A. Holland, E. Majer, J. Rubinstein, A. Sheehan, Coping with earthquakes induced by fluid injection. Science 347, 830–831 (2015)
F. Rall Walsh, III, Mark D. Zoback ,Oklahoma’s recent earthquakes and saltwater disposal.Sci. Adv.1,e1500195(2015).DOI:10.1126/sciadv.1500195
A. McGarr, Maximum magnitude earthquakes induced by fluid injection. J. Geophys. Res. 119, 1008–1019 (2014).